mlsanityData to 14 Oct 2026

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

arXiv 1807.11023 ↗First posted 29 Jul 2018cs.CR cs.LG cs.NIcited by 42

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Abstract (arXiv)

The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT system effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML /DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Jul 2018.

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Citing papers per year: 2021: 9; 2022: 8; 2023: 8; 2024: 9; 2025: 4; 2026: 4.

42 papers.

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  1. 2022Graph Neural Networks in IoT: A Surveycited by 28
  2. 2023NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscationcited by 16
  3. 2021Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluationcited by 11
  4. 2023Unsupervised Deep Learning for IoT Time Seriescited by 11
  5. 2024Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Surveycited by 10
  6. 2021Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Surveycited by 9
  7. 2021Machine Learning for Security in Vehicular Networks: A Comprehensive Surveycited by 7
  8. 2022Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threatscited by 7
  9. 2023A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainabilitycited by 7
  10. 2023Securing the Digital World: Protecting smart infrastructures and digital industries with Artificial Intelligence (AI)-enabled malware and intrusion detectioncited by 6
  11. 2021The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networkscited by 5
  12. 2021IoTDevID: A Behavior-Based Device Identification Method for the IoTcited by 4
30 more
  1. 2021Artificial Neural Network for Cybersecurity: A Comprehensive Reviewcited by 4
  2. 2022Latent Variable Models in the Era of Industrial Big Data: Extension and Beyondcited by 4
  3. 2025ChatIoT: Large Language Model-based Security Assistant for Internet of Things with Retrieval-Augmented Generationcited by 4
  4. 2022Generative Adversarial Learning for Trusted and Secure Clustering in Industrial Wireless Sensor Networkscited by 3
  5. 2021Utility Fairness for the Differentially Private Federated Learningcited by 2
  6. 2022Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directionscited by 2
  7. 2023Ensemble Learning based Anomaly Detection for IoT Cybersecurity via Bayesian Hyperparameters Sensitivity Analysiscited by 2
  8. 2024Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Surveycited by 2
  9. 2024Impact of Missing Values in Machine Learning: A Comprehensive Analysiscited by 2
  10. 2025Large Language Model-driven Security Assistant for Internet of Things via Chain-of-Thoughtcited by 2
  11. 2022Federated Learning Cost Disparity for IoT Devicescited by 1
  12. 2022Intrusion Detection in Internet of Things using Convolutional Neural Networkscited by 1
  13. 2024A Review of Cybersecurity Incidents in the Food and Agriculture Sectorcited by 1
  14. 2024Breaking Resource Barriers in Speech Emotion Recognition via Data Distillationcited by 1
  15. 2024Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challengescited by 1
  16. 2024Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learningcited by 1
  17. 2021Sensing and Mapping for Better Roads: Initial Plan for Using Federated Learning and Implementing a Digital Twin to Identify the Road Conditions in a Developing Country -- Sri Lankacited by 0
  18. 2021Secure solutions for Smart City Command Control Centre using AIOTcited by 0
  19. 2022A Review of the Convergence of 5G/6G Architecture and Deep Learningcited by 0
  20. 2023Discretization-based ensemble model for robust learning in IoTcited by 0
  21. 2023Unsupervised anomalies detection in IIoT edge devices networks using federated learningcited by 0
  22. 2023Is there a Trojan! : Literature survey and critical evaluation of the latest ML based modern intrusion detection systems in IoT environmentscited by 0
  23. 2024Detecting Compromised IoT Devices Using Autoencoders with Sequential Hypothesis Testingcited by 0
  24. 2024Securing the Future: Proactive Threat Hunting for Sustainable IoT Ecosystemscited by 0
  25. 2025Smart IoT Security: Lightweight Machine Learning Techniques for Multi-Class Attack Detection in IoT Networkscited by 0
  26. 2025Weaponizing Language Models for Cybersecurity Offensive Operations: Automating Vulnerability Assessment Report Validation; A Review Papercited by 0
  27. 2026Community-Based Model Sharing and Generalisation: Anomaly Detection in IoT Temperature Sensor Networkscited by 0
  28. 2026Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmapcited by 0
  29. 2026Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Codingcited by 0
  30. 2026F-ACVAE: A Federated Adaptive Conditional Variational Auto-Encoder for Privacy-Preserving Intrusion Detection in IoT Networkscited by 0